Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands.
Sequence Analysis Support Core, Leiden University Medical Center, Leiden, Zuid-Holland, 2333 ZC, The Netherlands.
Nat Commun. 2018 Aug 6;9(1):3097. doi: 10.1038/s41467-018-05452-6.
Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. Here, we develop a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). Analysis of genotype and whole-blood RNA-sequencing data from 3072 individuals identified 49 genes as drivers of downstream transcriptional changes (Wald P < 7 × 10), among which transcription factors were overrepresented (Fisher's P = 3.3 × 10). Our analysis suggests new gene functions and targets, including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6600 genes with a genetic instrument can be explored individually using a web-based browser.
确定调控基因网络背后的因果驱动因素对于理解基因功能至关重要。在这里，我们开发了一种方法，用于从观察性群体基因组学数据中大规模推断基因-基因相互作用，这些相互作用既具有方向性（使用局部遗传工具作为因果锚点，类似于孟德尔随机化），又具有特异性（通过控制连锁不平衡和多效性）。对 3072 个人的基因型和全血 RNA 测序数据进行分析，确定了 49 个基因作为下游转录变化的驱动因素（Wald P < 7×10），其中转录因子的比例过高（Fisher P = 3.3×10）。我们的分析表明了新的基因功能和靶点，包括 SENP7（涉及逆转录病毒抑制的锌指基因）和 BCL2A1（可能涉及听觉功能障碍的靶基因）。我们的工作强调了群体基因组学数据在推导有向基因表达网络方面的效用。可以使用基于网络的浏览器单独探索针对所有具有遗传工具的 6600 个基因的跨效资源。
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